TY - JOUR
T1 - Efficient Multi-agent Epistemic Planning: Teaching Planners About Nested Belief
AU - Muise, Christian
AU - Belle, Vaishak
AU - Felli, Paolo
AU - McIlraith, Sheila
AU - Miller, Tim
AU - Pearce, Adrian R.
AU - Sonenberg, Liz
N1 - Funding Information:
This research is partially funded by Australian Research Council Discovery Grant DP130102825, Foundations of Human-Agent Collaboration: Situation-Relevant Information Sharing, and by the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant 229717, Software, Devices, Data, and People: Working Together. We would like to also thank Biqing Fang, Yongmei Liu, Son Tran, and Francesco Fabiano for extensive help in understanding their systems and the differences between them and RP-MEP. We also thank Maayan Shvo for his help in characterizing the space of existing epistemic planners.
Funding Information:
This research is partially funded by Australian Research Council Discovery Grant DP130102825 , Foundations of Human-Agent Collaboration: Situation-Relevant Information Sharing, and by the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant 229717 , Software, Devices, Data, and People: Working Together. We would like to also thank Biqing Fang, Yongmei Liu, Son Tran, and Francesco Fabiano for extensive help in understanding their systems and the differences between them and RP-MEP. We also thank Maayan Shvo for his help in characterizing the space of existing epistemic planners.
Publisher Copyright:
© 2021 Elsevier B.V.
PY - 2022/1/1
Y1 - 2022/1/1
N2 - Many AI applications involve the interaction of multiple autonomous agents, requiring those agents to reason about their own beliefs, as well as those of other agents. However, planning involving nested beliefs is known to be computationally challenging. In this work, we address the task of synthesizing plans that necessitate reasoning about the beliefs of other agents. We plan from the perspective of a single agent with the potential for goals and actions that involve nested beliefs, non-homogeneous agents, co-present observations, and the ability for one agent to reason as if it were another. We formally characterize our notion of planning with nested belief, and subsequently demonstrate how to automatically convert such problems into problems that appeal to classical planning technology for solving efficiently. Our approach represents an important step towards applying the well-established field of automated planning to the challenging task of planning involving nested beliefs of multiple agents.
AB - Many AI applications involve the interaction of multiple autonomous agents, requiring those agents to reason about their own beliefs, as well as those of other agents. However, planning involving nested beliefs is known to be computationally challenging. In this work, we address the task of synthesizing plans that necessitate reasoning about the beliefs of other agents. We plan from the perspective of a single agent with the potential for goals and actions that involve nested beliefs, non-homogeneous agents, co-present observations, and the ability for one agent to reason as if it were another. We formally characterize our notion of planning with nested belief, and subsequently demonstrate how to automatically convert such problems into problems that appeal to classical planning technology for solving efficiently. Our approach represents an important step towards applying the well-established field of automated planning to the challenging task of planning involving nested beliefs of multiple agents.
KW - automated planning
KW - epistemic planning
KW - knowledge and belief
U2 - 10.1016/j.artint.2021.103605
DO - 10.1016/j.artint.2021.103605
M3 - Article
VL - 302
JO - Artificial Intelligence
JF - Artificial Intelligence
SN - 0004-3702
M1 - 103605
ER -